AIMC Topic: Image Processing, Computer-Assisted

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Quantitative salivary gland SPECT/CT using deep convolutional neural networks.

Scientific reports
Quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) using Tc-99m pertechnetate aids in evaluating salivary gland function. However, gland segmentation and quantitation of gland uptake is challenging. We develop a sa...

Features Guided Face Super-Resolution via Hybrid Model of Deep Learning and Random Forests.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Face hallucination or super-resolution is a practical application of general image super-resolution which has been recently studied by many researchers. The challenge of good face hallucination comes from a variety of poses, illuminations, facial exp...

Generative Deep Learning in Digital Pathology Workflows.

The American journal of pathology
Many modern histopathology laboratories are in the process of digitizing their workflows. Digitization of tissue images has made it feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern com...

A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic monitoring of retinal blood vessels proves very useful for the clinical assessment of ocular vascular anomalies or retinopathies. This paper presents an efficient and accurate deep learning-based method for vessel ...

EAR-UNet: A deep learning-based approach for segmentation of tympanic membranes from otoscopic images.

Artificial intelligence in medicine
This paper presents a method for automatic segmentation of tympanic membranes (TMs) from video-otoscopic images based on deep fully convolutional neural network. Built upon the UNet architecture, the proposed EAR scheme is based on three main paradig...

An anomaly detection approach to identify chronic brain infarcts on MRI.

Scientific reports
The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by le...

A deep learning approach to identifying immunogold particles in electron microscopy images.

Scientific reports
Electron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are typically used as an electron-dense marker for immunohistochemically labeled proteins. Manual annotat...

CEM500K, a large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning.

eLife
Automated segmentation of cellular electron microscopy (EM) datasets remains a challenge. Supervised deep learning (DL) methods that rely on region-of-interest (ROI) annotations yield models that fail to generalize to unrelated datasets. Newer unsupe...

Deep learning-based molecular morphometrics for kidney biopsies.

JCI insight
Morphologic examination of tissue biopsies is essential for histopathological diagnosis. However, accurate and scalable cellular quantification in human samples remains challenging. Here, we present a deep learning-based approach for antigen-specific...

Detection of COVID-19 from CT Lung Scans Using Transfer Learning.

Computational intelligence and neuroscience
This paper aims to investigate the use of transfer learning architectures in the detection of COVID-19 from CT lung scans. The study evaluates the performances of various transfer learning architectures, as well as the effects of the standard Histogr...